<p>Digital Twins (DTs) have brought rapid transformation in several engineering sectors; however, their application in geotechnical engineering remains limited due to the challenges related to real-time data integration, complexity of computation, standardisation issues, and immature technology. This study evaluates the current state of adoption of DTs in geotechnical engineering and proposes a comprehensive application-oriented workflow approach for creating a geotechnical DT. The research trends, technological advancements, and application domains were assessed using bibliometric analysis and an extensive literature survey. The findings of the study indicate that the application of DTs in the geotechnical field falls largely in low to mid technological readiness levels (TRL of 1 to 5), with most of the studies falling in the category of digital shadow or having limited DT implementations. A significant research need concerning uncertainty propagation, data interoperability, sensor reliability, cybersecurity, and field-scale application is identified by this study. Based on these findings, the study proposes a methodological framework that integrates real-time sensing networks, communication protocols, artificial intelligence (AI)/machine learning (ML)-based surrogate models, and data-driven and physics-informed decision-making for geotechnical assets monitoring and management. This study broadens the possible uses of DTs in problems, such as real-time slope deformation monitoring, tunnels and underground space planning, liquefaction hazard assessment, performance-based assessment of dams, embankments, foundations, etc. The article emphasises the limited research and utilisation of DTs while addressing the extensive opportunities for future development. It calls for action from researchers and industry professionals to explore the untapped potential of DTs in geotechnical engineering.</p> Graphical abstract <p></p>